scipy.linalg.eigvals_banded(a_band, lower=False, overwrite_a_band=False, select='a', select_range=None, check_finite=True)[source]

Solve real symmetric or complex hermitian band matrix eigenvalue problem.

Find eigenvalues w of a:

a v[:,i] = w[i] v[:,i]
v.H v    = identity

The matrix a is stored in a_band either in lower diagonal or upper diagonal ordered form:

a_band[u + i - j, j] == a[i,j] (if upper form; i <= j) a_band[ i - j, j] == a[i,j] (if lower form; i >= j)

where u is the number of bands above the diagonal.

Example of a_band (shape of a is (6,6), u=2):

upper form:
*   *   a02 a13 a24 a35
*   a01 a12 a23 a34 a45
a00 a11 a22 a33 a44 a55

lower form:
a00 a11 a22 a33 a44 a55
a10 a21 a32 a43 a54 *
a20 a31 a42 a53 *   *

Cells marked with * are not used.


a_band : (u+1, M) array_like

The bands of the M by M matrix a.

lower : bool, optional

Is the matrix in the lower form. (Default is upper form)

overwrite_a_band : bool, optional

Discard data in a_band (may enhance performance)

select : {‘a’, ‘v’, ‘i’}, optional

Which eigenvalues to calculate

select calculated
‘a’ All eigenvalues
‘v’ Eigenvalues in the interval (min, max]
‘i’ Eigenvalues with indices min <= i <= max

select_range : (min, max), optional

Range of selected eigenvalues

check_finite : bool, optional

Whether to check that the input matrix contains only finite numbers. Disabling may give a performance gain, but may result in problems (crashes, non-termination) if the inputs do contain infinities or NaNs.


w : (M,) ndarray

The eigenvalues, in ascending order, each repeated according to its multiplicity.



If eigenvalue computation does not converge.

See also

eigenvalues and right eigenvectors for symmetric/Hermitian band matrices
eigenvalues of symmetric/Hermitian tridiagonal matrices
eigenvalues of general arrays
eigenvalues and right eigenvectors for symmetric/Hermitian arrays
eigenvalues and right eigenvectors for non-symmetric arrays


>>> from scipy.linalg import eigvals_banded
>>> A = np.array([[1, 5, 2, 0], [5, 2, 5, 2], [2, 5, 3, 5], [0, 2, 5, 4]])
>>> Ab = np.array([[1, 2, 3, 4], [5, 5, 5, 0], [2, 2, 0, 0]])
>>> w = eigvals_banded(Ab, lower=True)
>>> w
array([-4.26200532, -2.22987175,  3.95222349, 12.53965359])